Enhancing low-light images via dehazing principles: Essence and method

被引:3
|
作者
Li, Fei [1 ]
Wang, Caiju [2 ]
Li, Xiaomao [1 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China
[2] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China
基金
中国国家自然科学基金;
关键词
Low-light image enhancement; Dehazing; Haze formation model; Image inversion;
D O I
10.1016/j.patrec.2024.07.017
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Given the visual resemblance between inverted low-light and hazy images, dehazing principles are borrowed to enhance low-light images. However, the essence of such methods remains unclear, and they are susceptible to over-enhancement. Regarding the above issues, in this letter, we present corresponding solutions. Specifically, we point out that the Haze Formation Model (HFM) used for image dehazing exhibits a Bidirectional Mapping Property (BMP), enabling adjustment of image brightness and contrast. Building upon this property, we give a comprehensive and in-depth theoretical explanation for why dehazing on inverted low-light image is a solution to the image brightness enhancement problem. Further, an Adaptive Full Dynamic Range Mapping (AFDRM) method is then proposed to guide HFM in restoring the visibility of low-light images without inversion, while overcoming the issue of over-enhancement. Extensive experiments validate our proof and demonstrate the efficacy of our method.
引用
收藏
页码:167 / 174
页数:8
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